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Congress: 2008
Author(s): Massei, Bakalowicz, Dupont
Corresponding author: Matthieu Fournier PhD in karst hydrology University of Rouen, UMR CNRS 6143 76821 Mont Saint Aignan Cedex Tel: 0235146662 / Fax: 0235147022

Keyword(s): karst aquifer, well water management, turbidity, wavelet analyses
AbstractIn many places throughout the world, drinking water frequently is contaminated by turbidity. Such turbidity, however, as representative of particle transport, can be used as to trace certain features of particle transport properties. Karst hydrosystems are well known for their vulnerability to turbidity due to their complex and unique characteristics which make them very different from other aquifers: high heterogeneity created and structured by groundwater flow, i.e., large voids, high flow velocities up to several hundreds of m.h−1, high flow rate springs up to some tens of m3.s−1 and most of all, a strong connection with surface watersheds. The great complexity of their functioning makes the characterization of their vulnerability difficult. This complexity results from the large number of parameters that can affect karst aquifer functioning, such as karst structure, boundary conditions, variations in base level, rainfall, human impact, and interactions with a river or the ocean. In order to investigate the particle transport properties in karst system, the karst hydrologist has at his disposal time series analyses like autocorrelation functions to show memory effect and cross-correlation functions to assess impulse response functions. Time series analyses have proven to be useful in improving understanding of karst systems. However with time series analyses, we lose the time evolution which have with hydrographs and we can not realize mass-balance. In this study, we use continuous wavelet analyses on turbidity annual dataset of a well used for drinking water supply to identify parameters which control turbidity released. The well is located in the chalk karst aquifer near the Seine river at 40 km of the Seine estuary in western Paris Basin. Results show that turbidity release at the well is decomposed into three components i) the rain event intensity, ii) the pumping periods and iii) the tidal range of Seine river. Time-domain reconstruction using inverse wavelet transform allows the assessment of the variance percentage explained by each component and show that most of 15% of annual turbidity flood at the well result of tidal range variation in the Seine river. These results demonstrate the impact of tidal range on transport properties to the karst aquifer and allow the identification of high vulnerability of well water. From these results, we can propose a new water ressource management for owner. To conclude, analysis using continuous wavelet spectra allowed a better investigation of occurrence periods, which could not be deduced from the time series analyses. Specific spectral components were isolated and a time-domain reconstruction using inverse wavelet transform allowed separation of turbidity origin (direct transfer of surface water or resuspension of intrakarstic sediments) according to their cause (rain event, pumping, tidal range).
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